scholarly journals Vehicle speed measurement by on-board acoustic signal processing

2018 ◽  
Vol 51 (5-6) ◽  
pp. 138-149 ◽  
Author(s):  
Hüseyin Göksu
Keyword(s):  
Relative Motion ◽  
Wavelet Packet ◽  
Absolute Error ◽  
Frequency Resolution ◽  
Vehicle Speed ◽  
Air Conditioner ◽  
Wavelet Packet Analysis ◽  
Time Frequency ◽  
Speed Sensor ◽  
Frequency Domain Methods

Estimation of vehicle speed by analysis of drive-by noise is a known technique. The methods used in this kind of practice generally estimate the velocity of the vehicle with respect to the microphone(s), so they rely on the relative motion of the vehicle to the microphone(s). There are also other methods that do not rely on this technique. For example, recent research has shown that there is a statistical correlation between vehicle speed and drive-by noise emissions spectra. This does not rely on the relative motion of the vehicle with respect to the microphone(s) so it inspires us to consider the possibility of predicting velocity of the vehicle using an on-board microphone. This has the potential for the development of a new kind of speed sensor. For this purpose we record sound signal from a vehicle under speed variation using an on-board microphone. Sound emissions from a vehicle are very complex, which is from the engine, the exhaust, the air conditioner, other mechanical parts, tires, and air resistance. These emissions carry both stationary and non-stationary information. We propose to make the analysis by wavelet packet analysis, rather than traditional time or frequency domain methods. Wavelet packet analysis, by providing arbitrary time-frequency resolution, enables analyzing signals of stationary and non-stationary nature. It has better time representation than Fourier analysis and better high-frequency resolution than Wavelet analysis. Subsignals from the wavelet packet analysis are analyzed further by Norm Entropy, Log Energy Entropy, and Energy. These features are evaluated by feeding them into a multilayer perceptron. Norm entropy achieves the best prediction with 97.89% average accuracy with 1.11 km/h mean absolute error which corresponds to 2.11% relative error. Time sensitivity is ±0.453 s and is open to improvement by varying the window width. The results indicate that, with further tests at other speed ranges, with other vehicles and under dynamic conditions, this method can be extended to the design of a new kind of vehicle speed sensor.

scholarly journals Flow Measurement by Wavelet Packet Analysis of Sound Emissions

2018 ◽  
Vol 51 (3-4) ◽  
pp. 104-112
Author(s):  
Hüseyin Göksu
Keyword(s):  
Flow Measurement ◽  
Acoustic Analysis ◽  
Wavelet Packet ◽  
Flow Speed ◽  
Frequency Resolution ◽  
Wavelet Packet Analysis ◽  
Resonance Effects ◽  
Time Frequency ◽  
Feature Vectors ◽  
Frequency Domain Methods

Fluid, when running through pipes, makes a complex sound emission whose parameters change nonlinearly with respect to flow speed. Especially, in household pipe systems, there may be spraying effects and resonance effects which make the emission more complex. We present a novel approach for predicting flow speed based on wavelet packet analysis of sound emissions rather than traditional time and frequency domain methods. Wavelet packet analysis, by providing arbitrary time–frequency resolution, enables analyzing signals of stationary and non-stationary nature. It has better time representation than Fourier analysis and better high-frequency resolution than wavelet analysis. Wavelet packet analysis subimages are further analyzed to obtain feature vectors of norm entropy. These feature vectors are fed into a multilayer perceptron for prediction. Prediction accuracy of 98.62%, with 3.99E−04 L/s mean absolute error and its corresponding 1.85% relative error is achieved. Time sensitivity is ±0.453 s and is open to improvement by varying window width. The result indicates that the proposed method is a good candidate for flow measurement by acoustic analysis.

scholarly journals Engine Speed–Independent Acoustic Signature for Vehicles

2018 ◽  
Vol 51 (3-4) ◽  
pp. 94-103 ◽  
Author(s):  
Hüseyin Göksu
Keyword(s):  
Multilayer Perceptron ◽  
Engine Speed ◽  
Wavelet Packet ◽  
Frequency Resolution ◽  
Wavelet Packet Analysis ◽  
Time Frequency ◽  
Feature Vectors ◽  
Energy Entropy ◽  
Acoustic Signature ◽  
Frequency Domain Methods

A vehicle, when running, makes a complex sound emission from the engine, the exhaust, the air conditioner, and other mechanical parts. Analysis of this sound for the purpose of vehicle identification is an interesting practice which has security- and transportation-related applications. Engine speed variation, which causes shifts in the frequency content of the emissions, makes Fourier-based methods ineffective in terms of providing a stable signature for the vehicle. We search for an engine speed–independent acoustic signature for the vehicle, and for this purpose, we propose wavelet packet analysis rather than traditional time- or frequency-domain methods. Wavelet packet analysis, by providing arbitrary time–frequency resolution, enables analyzing signals of stationary and non-stationary nature. It has better time representation than Fourier analysis and better high-frequency resolution than wavelet analysis. Under varying engine speed, sound emissions are recorded from four cars and analyzed by wavelet packet analysis. Wavelet packet analysis subimages are further analyzed to obtain feature vectors in the form of log energy entropy, norm entropy, and energy. These feature vectors are fed into a classifier, multilayer perceptron, for evaluation. While norm entropy achieves a classification rate of 100%, log energy entropy and energy achieves classification rates of 99.26% and 97.79%, respectively. These results indicate that, wavelet packet analysis along with norm entropy and multilayer perceptron provides an accurate vehicle-specific acoustic signature independent of the engine speed.

MECHANICAL WATCH SIGNATURE ANALYSIS BASED ON WAVELET DECOMPOSITION

2009 ◽  
Vol 07 (04) ◽  
pp. 491-512 ◽  
Author(s):  
QINGBO HE ◽  
RUXU DU
Keyword(s):  
Acoustic Signal ◽  
Wavelet Decomposition ◽  
Wavelet Packet ◽  
Wide Frequency Range ◽  
Frequency Resolution ◽  
Signature Analysis ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Frequency Components ◽  
Wavelet Techniques

The acoustic signal of mechanical watch is a distinct multi-component signal. It contains many frequency components corresponding to specific escapement motion sources with a very wide frequency range. Therefore, it is challenging for signature analysis of mechanical watch by the acoustic signal. This paper studies the time-frequency signatures of the mechanical watch based on wavelet decomposition. Two methods are proposed to improve the frequency resolution of traditional wavelet techniques by combining other beneficial techniques in the sense of decomposing specific mono- or independent components. The empirical mode decomposition (EMD) is presented to advance the wavelet packet decomposition (WPD) to decompose the mono-component signals. And the blind source separation (BSS) makes the redundancy of continuous wavelet transform (CWT) further gain good frequency resolution in the independent meaning. The decomposed signals by the two methods reveal the insight of mechanical watch movement and can contribute much simpler and clearer time-frequency signatures. Experimental results indicated the effectiveness of the two methods and the value of the time-frequency signatures in analyzing and diagnosing mechanical watch movements.

Wavelet Packet Analysis Based Feature Extraction of Vehicular Acoustic Signal

2011 ◽  
Vol 55-57 ◽  
pp. 1593-1598
Author(s):  
Xiao Xuan Qi ◽  
Jian Wei Ji ◽  
Xiao Wei Han ◽  
Zhong Hu Yuan
Keyword(s):  
Acoustic Signal ◽  
Rbf Neural Network ◽  
Wavelet Packet ◽  
Wavelet Packet Analysis ◽  
Moving Vehicle ◽  
Time Frequency ◽  
Vehicle Type ◽  
Feature Information ◽  
Vehicle Type Classification ◽  
Type Classification

In this paper, an approach based on wavelet packet analysis is proposed to deal with the problem that acoustic signal of moving vehicle is easily influenced by environmental noise in vehicle type classification. Wavelet packet analysis is applied to extract local and detail feature information of acoustic signal in the time-frequency domain. Firstly, raw acoustic signal is decomposed into different frequency bands by wavelet packet analysis, and then decomposition coefficients are reconstructed. The energy of every frequency band component is used to form the feature vector. Finally, vehicle type classification is implemented by RBF neural network on the basis of these feature vectors. Experimental results show that the proposed method is feasible and effective.

The Improvement of Wavelet Packet for Psychoacoustic Model

2012 ◽  
Vol 591-593 ◽  
pp. 2491-2494
Author(s):  
Jing Lei Zhou ◽  
Ying Li
Keyword(s):  
Spectrum Analysis ◽  
Wavelet Packet ◽  
Critical Band ◽  
Frequency Resolution ◽  
Wavelet Packet Decomposition ◽  
Frequency Division ◽  
Time Frequency ◽  
Psychoacoustic Model ◽  
Insufficient Time ◽  
The Way

Compared with common psychoacoustic model, this article uses wavelet packet decomposition to decompose a signal. This method improves the situation of insufficient time-frequency resolution which the uniform spectrum analysis causes. In addition, frequency division by wavelet packet decomposition is much closer to human’s critical band than the way common psychoacoustic model getting, it is more suitable to human’s hearing characteristics. So we can use wavelet packet decomposition replace FFT in MPEG, and get accurate psychoacoustic model.

An Experimental Study on Working State Recognition of Machine Tools

2012 ◽  
Vol 201-202 ◽  
pp. 707-710
Author(s):  
Teng Fei Fang ◽  
Guo Fu Li ◽  
Lei Wang ◽  
Hong Bin Li ◽  
Wei Guo
Keyword(s):  
Frequency Analysis ◽  
Machine Tools ◽  
Wavelet Packet ◽  
Current Signal ◽  
Wavelet Packet Analysis ◽  
Time Frequency Analysis ◽  
State Recognition ◽  
Time Frequency ◽  
Different Characteristics ◽  
One Machine

In order to obtain the real-time working state of machine tools, this experiment extracted the characteristics of machine tools using joint time-frequency analysis and wavelet packet analysis for the total current signal collected, to distinguish which machine is running. First, use joint time-frequency analysis on signal of a single machine to get different characteristics. And find some frequency points with amplitude changing significantly, preparing for the subsequent experiment. Then use wavelet packet analysis on the total signal of more than one machine, finding more obvious characteristics of the different machines with different speeds. Thus it is easy to identify which machine is working. By this experiment, we can save labor, improve efficiency and integrate information in system conveniently.

Application of Wavelet Analysis to Fault Diagnosis of Mechanical Equipment

2012 ◽  
Vol 590 ◽  
pp. 325-328
Author(s):  
Shan Zhen Xu ◽  
Cheng Wang
Keyword(s):  
Wavelet Analysis ◽  
Simulation Analysis ◽  
Wavelet Packet ◽  
Gear Transmission ◽  
Good Time ◽  
Mechanical Equipment ◽  
Wavelet Packet Analysis ◽  
Time Frequency ◽  
Gear Fault ◽  
Adaptive Ability

Aiming at the characteristics of mechanical gear transmission, taking automobile main reducer as research object, this paper analyzes the gear transmission of failure mechanism and failure characteristics. According to the good time frequency character and adaptive ability of wavelet analysis, it proposes gear fault information extraction method based on wavelet packet analysis and have carried out simulation analysis. The results show that the method of wavelet packet analysis can effectively detect mutations in the signal part and noise to achieve the diagnosis of mechanical system failures.

Intelligent Identification Based on Wavelet Packet Decomposition and Adaptive Particle Swarm Optimization SVM

2013 ◽  
Vol 380-384 ◽  
pp. 4043-4046
Author(s):  
Qiang Wang ◽  
Li Jing Ren
Keyword(s):  
Support Vector Machine ◽  
Flow Regime ◽  
Wavelet Packet ◽  
Particle Swarm ◽  
Flow Regimes ◽  
Frequency Resolution ◽  
Support Vector ◽  
Wavelet Packet Decomposition ◽  
Horizontal Pipe ◽  
Time Frequency

In this paper, a new Intelligent Identification method based on wavelet packet decomposition and APSO-SVM was put forward. As is known the characteristic of pressure drop is nonlinear and non-stationary. The wavelet packet transform can decompose signals to different frequency bands according to any time frequency resolution ratio, the features are extracted from the differential pressure fluctuation signals of the air-water two-phase flow in the horizontal pipe and the wavelet packet energy features of various flow regimes are obtained. The adaptive particle swarm ptimization support vector machine was trained using these eigenvectors as flow regime samples, and the flow regime intelligent identification was realized. The test results show the wavelet packet energy features can excellently reflect the difference between four typical flow regimes, and successful training the support vector machine can quickly and accurately identify four typical flow regimes. So a new way to identify flow regime by soft sensing is proposed.

scholarly journals A comparison of model and non-model based time-frequency transforms for sperm whale click classification

2007 ◽  
Vol 87 (1) ◽  
pp. 27-34 ◽  
Author(s):  
M. van der Schaar ◽  
E. Delory ◽  
J. van der Weide ◽  
C. Kamminga ◽  
J.C. Goold ◽  
...  
Keyword(s):  
Wavelet Packet ◽  
Moving Average ◽  
Sperm Whale ◽  
Interesting Property ◽  
Frequency Resolution ◽  
Optimal Time ◽  
Model Parameters ◽  
Time Frequency ◽  
Gabor Function ◽  
Model Based

We tried to find discriminating features for sperm whale clicks in order to distinguish between clicks from different whales, or to enable unique identification. We examined two different methods to obtain suitable characteristics. First, a model based on the Gabor function was used to describe the dominant frequencies in a click, and then the model parameters were used as classification features. The Gabor function model was selected because it has been used to model dolphin sonar pulses with great precision. Additionally, it has the interesting property that it has an optimal time–frequency resolution. As such, it can indicate optimal usage of the sonar by sperm whales. Second, the clicks were expressed in a wavelet packet table, from which subsequently a local discriminant basis was created. A wavelet packet basis has the advantage that it offers a highly redundant number of coefficients, which allow signals to be represented in many different ways. From the redundant signal description a representation can be selected that emphasizes the differences between classes. This local discriminant basis is more flexible than the Gabor function, which can make it more suitable for classification, but it is also more complex. Class vectors were created with both models and classification was based on the distance of a click to these vectors. We show that the Gabor function could not model the sperm whale clicks very well, due to the variability of the changing click characteristics. Best performance was reached when three subsequent clicks were averaged to smoothen the variability. Around 70% of the clicks classified correctly in both the training and validation sets. The wavelet packet table adapted better to the changing characteristics, and gave better classification. Here, also using a 3-click moving average, around 95% of the training sets classified correctly and 78% of the validation sets. These numbers lowered by only a few per cent when single clicks, instead of a moving average, were classified. This indicates that, while the features may show too much variability to enable unique identification of individual whales on a click by click basis, the wavelet approach may be capable of distinguishing between a small group of whales.

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